rwkv-architecture

RWKV - Receptance Weighted Key Value

Quick start

RWKV (RwaKuv) combines Transformer parallelization (training) with RNN efficiency (inference).

Installation:

# Install PyTorch
pip install torch --upgrade --extra-index-url https://download.pytorch.org/whl/cu121

# Install dependencies
pip install pytorch-lightning==1.9.5 deepspeed wandb ninja --upgrade

# Install RWKV
pip install rwkv

Basic usage (GPT mode + RNN mode):

import os
from rwkv.model import RWKV

os.environ["RWKV_JIT_ON"] = '1'
os.environ["RWKV_CUDA_ON"] = '1'  # Use CUDA kernel for speed

# Load model
model = RWKV(
    model='/path/to/RWKV-4-Pile-1B5-20220903-8040',
    strategy='cuda fp16'
)

# GPT mode (parallel processing)
out, state = model.forward([187, 510, 1563, 310, 247], None)
print(out.detach().cpu().numpy())  # Logits

# RNN mode (sequential processing, same result)
out, state = model.forward([187, 510], None)  # First 2 tokens
out, state = model.forward([1563], state)      # Next token
out, state = model.forward([310, 247], state)  # Last tokens
print(out.detach().cpu().numpy())  # Same logits as above!

Common workflows

Workflow 1: Text generation (streaming)

Efficient token-by-token generation:

from rwkv.model import RWKV
from rwkv.utils import PIPELINE

model = RWKV(model='RWKV-4-Pile-14B-20230313-ctx8192-test1050', strategy='cuda fp16')
pipeline = PIPELINE(model, "20B_tokenizer.json")

# Initial prompt
prompt = "The future of AI is"
state = None

# Generate token by token
for token in prompt:
    out, state = pipeline.model.forward(pipeline.encode(token), state)

# Continue generation
for _ in range(100):
    out, state = pipeline.model.forward(None, state)
    token = pipeline.sample_logits(out)
    print(pipeline.decode(token), end='', flush=True)

Key advantage: Constant memory per token (no growing KV cache)

Workflow 2: Long context processing (infinite context)

Process million-token sequences:

model = RWKV(model='RWKV-4-Pile-14B', strategy='cuda fp16')

# Process very long document
state = None
long_document = load_document()  # e.g., 1M tokens

# Stream through entire document
for chunk in chunks(long_document, chunk_size=1024):
    out, state = model.forward(chunk, state)

# State now contains information from entire 1M token document
# Memory usage: O(1) (constant, not O(n)!)